A Hybrid Tucker-LSTM Tensor Network Model for SOC Prediction in Electric Vehicles

TL;DR

This paper introduces a hybrid Tucker-LSTM tensor network model that significantly improves SOC prediction accuracy in electric vehicles by effectively reducing data dimensionality and capturing temporal dynamics.

Contribution

It presents a novel combination of Tucker tensor decomposition with LSTM networks for SOC prediction, outperforming standard methods on multiple metrics.

Findings

70.5% reduction in MSE

48.7% improvement in MAE

Tensor decomposition preserves data fidelity

Abstract

Accurate state of charge estimation is critical for the success of electric vehicle battery management strategies, but it is well known that conventional estimators suffer from two fundamental shortcomings: cumulative errors that grow over time and reliance on simplified battery models that do not reflect real world dynamics. Therefore, this paper presents a novel hybrid approach combining Tucker tensor decomposition with LSTM networks, using full - lifecycle EV field data for SOC prediction. The inputs are charge status, mileage, voltage, current, cell differentials, and temporal features. Tucker decomposition is skillfully used to reduce dimensionality while maintaining the temporal structure, hence allowing a direct, fair comparison with standard LSTM. The result is unequivocal: Tucker - LSTM outperforms the baseline on all metrics, with MSE dropping 70.5\% (from 21.07 to 6.22 ), MAE improving 48.7\% (from 3.37\% to 1.73\%), RMSE falling from 4.59\% to 2.49\%, and $R^2$ rising from 0.918 to 0.976. Since the experimental results demonstrably demonstrate that tensor decomposition compresses high-dimensional battery data very well without loss of predictive fidelity, this paper naturally opens up a new direction for tensor-based analytics in electric vehicle battery management.